Systems and methods for processing items in a queue

ABSTRACT

Systems and methods for processing items in a queue, a system including one or more memory devices storing instructions and one or more processors configured to execute the instructions to perform operations including: analyzing training data to build a predictive model; applying the predictive model to items in a queue to determine scores of the items based on respective probabilities of an entity completing an action for each item; listing the items, sorted by the scores, in a first display view; identifying a first and a second item, respectively having highest and next highest scores; grouping, with each of the first and second items, items that satisfy a grouping condition based on characteristic information of the items; and listing the groups of items including the first and second items, sorted based on the scores of the first and second items, in a second display view.

CROSS-REFERENCE TO RELATED APPLICATION

This application relates to U.S. patent application Ser. No. 16/660,751,filed Oct. 22, 2019, titled “SYSTEMS AND METHODS FOR INTELLIGENTLYOPTIMIZING A QUEUE OF ACTIONS IN AN INTERFACE USING MACHINE LEARNING,”the disclosure of which is expressly incorporated herein by reference inits entirety.

TECHNICAL FIELD

The present disclosure generally relates to data processing systems, andmore particularly, to systems and methods for processing items in aqueue.

BACKGROUND

Consumers often seek financing from institutions to finance certaintypes of purchases, including, for example, automobile purchases.Automobile dealerships may act as loan-arranging entities that helparrange the financing, and they typically send loan applications tomultiple financial institutions (e.g., prospective lenders), who maycompetitively bid to fund the loan applications. Relationship managersor loan officers working for the financial entities may evaluate theloan applications and seek to originate the loans on terms that areacceptable to the financial entities, the dealers, and the consumers.

A relationship manager may contact the dealerships regarding the loanapplications assigned to the relationship manager to help improve alikelihood that the loan applications will be funded through thefinancial entity for which the relationship manager works. Existingtools for assisting the relationship manager have deficiencies, such aslacking the ability to prioritize the loan applications for review bythe relationship manager. Due to a high volume of loan applicationspending review, the relationship manager may not be able to evaluate allapplications manually and therefore relies solely on his or herexperience when prioritizing and evaluating the loan applications.

SUMMARY

In the following description, certain aspects and embodiments of thepresent disclosure will become evident. It should be understood that thedisclosure, in its broadest sense, could be practiced without having oneor more features of these aspects and embodiments. It should also beunderstood that these aspects and embodiments are merely exemplary.

The disclosed embodiments include a system for processing items in aqueue. In some embodiments, the system includes one or more memorydevices storing instructions and one or more processors. The one or moreprocessors are configured to execute the instructions to performoperations including: analyzing training data to build a predictivemodel; applying the predictive model to a plurality of items in a queueof items to determine scores of the plurality of items based onrespective probabilities of an entity completing an action for each ofthe plurality of items; listing the plurality of items, sorted by thescores, in a first display view to a user through a user interface;identifying a first and a second item, of the plurality of items,respectively having highest and next highest scores; grouping, with eachof the first and second items, other ones of the plurality of items thatsatisfy a grouping condition based on characteristic information of theplurality of items; and listing the groups of items including the firstand second items, sorted based on the scores of the first and seconditems, in a second display view to the user through the user interface.

The disclosed embodiments also include a method for processing items ina queue. In some embodiments, the method includes receivingcharacteristic information of a plurality of items; applying apredictive model to the plurality of items to determine scores of theplurality of items based on respective probabilities of an entitycompleting an action for each of the plurality of items; listing theplurality of items, sorted by the scores, in a first display view to auser through a user interface; identifying a first and a second item, ofthe plurality of items, respectively having highest and next highestscores; grouping, with each of the first and second items, other ones ofthe plurality of items that satisfy a grouping condition based oncharacteristic information of the plurality of items; and listing thegroups of items including the first and second items, sorted based onthe scores of the first and second items, in a second display view tothe user through the user interface.

The disclosed embodiments further include a non-transitorycomputer-readable medium, storing instructions that are executable byone or more processors to perform operations. The operations include:determining scores of a plurality of items based on respectiveprobabilities of an entity completing an action for each of theplurality of items; listing the plurality of items, sorted by thescores, in a first display view to a user through a user interface;identifying a first and a second item of the plurality of itemsrespectively having highest and next highest scores; grouping, with eachof the first and second items, other ones of the plurality of items thatsatisfy a grouping condition based on characteristic information of theplurality of items; and listing the groups of items including the firstand second items, sorted based on the scores of the first and seconditems, in a second display view to the user through the user interface.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory only,and are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate exemplary disclosed embodimentsand, together with the description, serve to explain the disclosedembodiments. In the drawings:

FIG. 1 is a schematic diagram illustrating an exemplary system forprocessing items in a queue, consistent with some embodiments of thepresent disclosure.

FIG. 2 is a schematic diagram illustrating an exemplary cloud-computingsystem, consistent with some embodiments of the present disclosure.

FIG. 3 is a flowchart of an exemplary method of processing items in thequeue, consistent with some embodiments of the present disclosure.

FIG. 4 is a schematic diagram illustrating an exemplary user interfacein a first display view, consistent with some embodiments of the presentdisclosure.

FIG. 5 is a schematic diagram illustrating an exemplary user interfacein a second display view, consistent with some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

An employee working in an entity may utilize a tool or a platform toassist reviewing and tracking status or pending action items of multipleongoing projects or cases. For example, a relationship manager of afinancial entity may be assigned to handle multiple loan applicationsthat need to be reviewed and processed. Sometimes the relationshipmanager may need to negotiate with loan arranging entities thatsubmitted the loan applications.

Existing tools lack the ability to help the relationship managerprioritize actions or loan applications, and the relationship managerrelies on his or her experience to decide which loan applications towork on. Since experience and behavior vary from person to person, thecurrent workflow cannot optimize the likelihood of winning the bid tofund loan applications.

In some embodiments of the present disclosure, improved systems andmethods for processing items in a queue use machine learning to rank theitems and list the items in different ways based on one or morecharacteristics of the items to overcome problems stated above. Forexample, the improved systems and methods may use machine learning topredict the probability and/or profitability of loan applications andprovide a friendly user interface with different views for therelationship manager to facilitate engaging with different loanarranging entities to negotiate the loan applications. The followingdisclosure provides exemplary systems and methods for processing itemsin a queue for realizing at least some of the above advantages andbenefits over conventional systems.

Reference will now be made to exemplary embodiments, examples of whichare illustrated in the accompanying drawings and disclosed herein.Wherever convenient, the same reference numbers will be used throughoutthe drawings to refer to the same or like parts.

FIG. 1 is a schematic diagram illustrating an exemplary system 100 forprocessing items in a queue, consistent with some embodiments of thepresent disclosure. System 100 may be an intelligent interface queuingsystem and include a processing device 120, an interface 140, and one ormore databases 160. Processing device 120 can include one or moreprocessors 122 and one or more memory devices 124. Processing device 120may also store one or more predictive models 126 and one or more workingqueues 128.

In some embodiments, processor 122 may be implemented byapplication-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), or various other types of processors. For example,processor 122 can be one or more central processors or microprocessors.

Memory device(s) 124 can be various computer-readable media implementedin any method or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Memory device(s) 124 can include non-volatile media and/orvolatile media, such as read only memory (ROM) and random-access memory(RAM), and be communicatively coupled with processor 122. A basicinput/output system (BIOS) containing the basic routines that help totransfer information between elements within system 100 is typicallystored in ROM. Additionally, RAM may contain operating system (OS),applications, other programmable code, and programs. The RAM typicallycontains data and/or program modules that are immediately accessible toand/or presently being operated on by processor 122.

In some embodiments, memory device(s) 124 may include a main memory,which can be used for storing temporary variables or other intermediateinformation during execution of instructions by processor 122. Suchinstructions, after being stored in non-transitory storage mediaaccessible to processor 122, render system 100 into a special-purposemachine that is customized to perform operations specified in theinstructions. The term “non-transitory media” as used herein refers toany non-transitory media storing data or instructions that cause amachine to operate in a specific fashion. Such non-transitory mediainclude, for example, optical or magnetic disks, dynamic memory, afloppy disk, a flexible disk, hard disk, solid state drive, magneticcassettes, magnetic tape, or any other magnetic data storage medium, aCD-ROM, digital versatile disks (DVD) or any other optical data storagemedium, any physical medium with patterns of holes, a Random AccessMemory (RAM), a read-only memory (ROM), a Programmable Read-Only Memory(PROM), a EPROM, a FLASH-EPROM, NVRAM, flash memory, or other memorytechnology and/or any other storage medium with the same functionalitythat can be contemplated by persons of ordinary skill in the art towhich this disclosure pertains.

In some embodiments, predictive model(s) 126 and working queue(s) 128can be stored in memory device(s) 124 or other storage device(s), suchas a hard disk or solid-state drive, in system 100. In some embodiments,predictive model(s) 126 and working queue(s) 128 may be stored in anexternal storage device in communication with system 100 via a networkor interface 140. For example, predictive model(s) 126 and workingqueue(s) 128 may also be stored in database(s) 160.

Interface 140 can include one or more interfaces, such as a datainterface 142, a user interface 144, and an application interface 146,to interact with database(s) 160, users, and other computing systems viawired or wireless communication. As shown in FIG. 1, database(s) 160 iscommunicatively coupled to processing device 120 through data interface142 in interface 140. User interface 144 may be, for example, a website,a web-based application, a mobile application, a desktop application,and/or other software, running on a personal computer, smartphone,tablet, kiosk system, or other electronic device communicatively coupledto processing device 120, which provides a graphic user interface tointeract with users to input commands and display information.Application interface 146 may be any hardware or software interfacecommunicatively coupled to processing device 120 and configured toconnect processing device 120 with other applications.

In some embodiments, system 100 may operate as a part of acloud-computing system. FIG. 2 is a schematic diagram illustrating anexemplary cloud-computing system 200, consistent with some embodimentsof the present disclosure. Cloud-computing system 200 includes one ormore computing resources 220, one or more databases 240, an interface260, a network 280, and the like. In some embodiments, cloud-computingsystem 200 may provide a Software as a Service (SaaS) platform toexecute one or more applications to perform a method for process itemsin a queue.

In some embodiments, components in cloud-computing system 200 can beused to implement system 100 for processing items in a queue. Forexample, computing resource(s) 220 may include one or more processingdevices 120 and corresponding processor(s) 122, memory device(s) 124,predictive model(s) 126, and working queue(s) 128. Database 240 can beconfigured to store database(s) 160 as sub database(s). Interface 260and network 280 can utilize data interface 142, user interface 144, andapplication interface 146 by exchanging data or instructions betweencloud-computing system 200 and other systems.

Computing resources 220 can include one or more computing devicesconfigurable to train data models. For example, computing resources 220may include one or more processing devices 120 and correspondingprocessor(s) 122, memory device(s) 124, predictive model(s) 126, andworking queue(s) 128 shown in FIG. 1. The computing devices can bespecial-purpose computing devices, such as graphical processing units(GPUs), ASICs, or general-purpose computing devices. The computingdevices can be configured to host an environment for training datamodels. For example, the computing devices can host virtual machines,pods, or containers. The computing devices can be configured to runapplications for generating data models. For example, the computingdevices can be configured to run Amazon Web Services (AWS) SageMaker,Tensorflow, or similar machine-learning training applications. Computingresources 220 can be configured to receive models for training frommodel optimizers, model storage devices, or other components ofcloud-computing system 200. Computing resources 220 can be configured toprovide training results, including trained models and modelinformation, such as the type and/or purpose of the model and anymeasures of classification error.

Database 240 can include one or more databases configured to store datafor use by cloud-computing system 200. For example, database 240 maystore training data for machine learning or neural network training. Thedatabases can include cloud-based databases (e.g., AMAZON WEB SERVICESS3 buckets) or on-premises databases.

Interface 260 can be configured to provide communications with othersystems and to manage interactions between cloud-computing system 200and other systems using network 280. In some aspects, interface 260 canbe configured to publish data received from other components ofcloud-computing system 200 (e.g., computing resources 220, database 240,or the like). This data can be published in a publication andsubscription framework (e.g., using APACHE KAFKA), through a networksocket, in response to queries from other systems, or using other knownmethods. In various aspects, interface 260 can be configured to providedata or instructions received from other systems to components ofcloud-computing system 200. For example, interface 260 can be configuredto receive instructions for generating data models (e.g., type of datamodel, data model parameters, training data indicators, trainingparameters, or the like) from another system and provide thisinformation to computing resources 220. As an additional example,interface 260 can be configured to receive data including sensitiveportions from another system (e.g. in a file, a message in a publicationand subscription framework, a network socket, or the like) and providethat data to database 240.

Network 280 provides communication between components (e.g., computingresource(s) 220, database(s) 240, and interface 260) of cloud-computingsystem 200. Accordingly, components of cloud-computing system 200 can beconfigured to communicate with each other, or with external componentsof system 200. Network 280 can include any combination of electronicscommunications networks enabling communication between components ofcloud-computing system 200. For example, network 280 may include theInternet and/or any type of wide area network, an intranet, ametropolitan area network, a local area network (LAN), a wirelessnetwork, a cellular communications network, a Bluetooth network, a radionetwork, a device bus, or any other type of electronics communicationsnetwork know to one of skill in the art.

The arrangement of components depicted in FIG. 2 is merely an exampleand not intend to limit the present disclosure. In various embodimentsof the present disclosure, cloud-computing system 200 can includeadditional components, or fewer components. Multiple components ofcloud-computing system 200 can be implemented using the same physicalcomputing device or different physical computing devices. It should beappreciated that cloud-computing system 200 is depicted merely as anexample for implementing a cloud-computing system.

As mentioned above, components in cloud-computing system 200 can be usedto implement system 100 to carry out various operations disclosedherein. Particularly, the operations may be performed by one or moreprocessor 122 in computing resource(s) 220 of cloud-computing system200. For illustrative purposes, the description below may referenceprocessor 122 and system 100 as the system configured to carry outvarious operations. It should be appreciated, however, that thereferences to processor 122 and system 100 are merely provided asexamples and are not meant to limit the present disclosure.

Referring to FIG. 1 again, in some embodiments, processor 122 can beconfigured to execute a set of instructions stored in memory device(s)124 to cause system 100 to perform operations for processing items in aqueue. Processor 122 can analyze a set of training data to buildpredictive model 126 using one or more machine learning algorithms.Predictive model 126 may be applied to determine a score indicating alikelihood for completing a task or an action between two parties for anitem.

Particularly, in some embodiments, processor 122 may utilize predictivemodel 126 to analyze historical data to identify the likelihood forcompleting the task or the action to determine the score, which can beused to prioritize the items and tasks for an entity.

In some embodiments, processor 122 may also utilize predictive model 126to consider or learn several factors relevant to the completion of thetask or action. For example, in some embodiments, predictive model 126may take into account the interactions between two parties, e.g., afirst party and a second party, to determine the score. In someembodiments, predictive model 126 may also take into account whether thefirst party proactively or reactively engaged with the second party todetermine the score.

In some embodiments, the items may be loan applications involving tasksor actions between two parties. For example, the first party may includea relationship manager working for a financial entity, the second partymay include one or more entities arranging the loan applications (e.g.,dealerships), and the tasks or actions may include one or more loanapplications pending review by the relationship manager. The loanapplications can be, but are not limited to, automobile loanapplications for financing the purchases of cars, trucks, or otherautomotive vehicles. It is noted that term “financial entity” is usedherein in its broadest sense and is intended to encompass all suchpossibilities including, but not limited to, banks, credit card companyor credit unions, financial institutions, finance companies, stockbrokerages, savings and loans, mortgage companies, insurance companies,or any other type of financial service entity. Institutions,organizations, or even individuals may be varied in their organizationand structure.

Processor 122 may apply predictive model 126 to these loan applicationsto determine the scores of these loan applications to the financialentity and prioritize these loan applications accordingly. Processor 122may therefore provide the abilities to analyze historical data andprioritize future tasks, both of which cannot not be done by therelationship manager manually or mentally due to the high volume of loanapplications and because most of the historical data is recorded in amanner not readily understandable by the relationship manager.Furthermore, the relationship manager does not have the mental capacityto keep track of all interactions between the relationship manager andthe various dealerships, let alone analyzing these interactions toidentify factors relevant to the completion of the loan applications.Processor 122 can thus be utilized to improve the way the relationshipmanagers manage and prioritize the loan applications pending theirreview.

In some embodiments, scores of the loan applications may be determinedby taking into consideration the probabilities of the financial entityfunding the loan applications. In some embodiments, predictive model 126may predict the probabilities of the financial entity funding the loanapplications both for situations in which the financial entity, throughactions by the relationship manager, (1) proactively engages the loanarranging entities (e.g., the dealerships) to negotiate the terms and(2) reactively engages the loan arranging entities to negotiate theterms. In this manner, processor 122 may utilize predictive model 126 todetermine whether proactively engaging the loan arranging entities canpositively affect the financial entity's likelihood of winning the bidto fund the loan applications.

In some embodiments, processor 122 may rank prediction results and storeworking queue(s) 128 for one or more relationship managers based on thescores of the items. By reporting and presenting the prediction resultsto the relationship manager(s), system 100 can help the relationshipmanager(s) decide whether to proactively engage with the correspondingloan arranging entity for any of the loan applications, having pendingactions, in the respective working queue(s). For example, processor 122may present the loan applications according to a ranking order, based onworking queue(s) 128, to the relationship managers through userinterface 144. In addition, in different display views, the rankingorder presented in user interface 144 may be different to facilitateengagements with different loan arranging entities.

In some embodiments, processor 122 may build predictive model 126 as agradient boosting machine (GBM) model. Gradient boosting is amachine-learning technique for regression and classification. In someembodiments, the gradient boosting machine is configured to producepredictive model 126 as an ensemble of predictive models, such asdecision trees, which can be utilized to predict the probabilities ofthe financial entity funding a loan application both with and withoutproactive engagement of a relationship manager working for the financialentity.

It is contemplated that, while the GBM model is used as an exampleherein, processor 122 may build predictive model 126 using othermachine-learning techniques without departing from the spirit and scopeof the present disclosure. For example, processor 122 may buildpredictive model 126 using logistic regression, naive Bayes, neuralnetworks (e.g., multilayer perceptron, back-propagation, and other deeparchitectures), support vector machines, random forests, as well asother boosting algorithms or binary classification algorithms that canbe adapted to output probabilities.

It is also to be understood that processor 122 may be configured tobuild more than one predictive model 126 depending on the needs of thefinancial entity utilizing system 100. For example, for a smallfinancial entity having only a few relationship managers, processor 122can be configured to build just one predictive model 126 to collectivelyprocess all loan applications, having pending actions, by the financialentity. On the other hand, for a large financial entity having differentlending units for processing different types of loans, processor 122 canbe configured to build one or more predictive models 126 for therespective lending units. Alternatively, or additionally, processor 122can be configured to build one predictive model 126 for eachrelationship manager. Likewise, in some embodiments, processor 122 canalso be configured to build different predictive models 126 fordifferent dealerships with which the financial entity conducts business,or for different geographic regions or other types of clusters (e.g.,clusters based on credit profiles such as prime, near prime, subprime,etc.), without departing from the spirit and scope of the presentdisclosure.

In some embodiments, processor 122 may specify a dependent variable ofpredictive model 126 to represent an outcome of the loan applications.For example, in some embodiments, the outcome may indicate whether aloan application has reached the status of “funded” or “not funded”within a specified time period (e.g., 30 days) from an applicationsubmission date. In some embodiments, unfunded loan applications mayexpire after the time period has lapsed after their respectivesubmission dates.

Processor 122 can build predictive model 126 using training datacontaining historical loan applications. The historical loanapplications may include applications that have either been funded bythe financial entity or have expired or rejected without being funded bythe financial entity. In some embodiments, processor 122 may retrievethe historical loan applications from database(s) 160 maintained by thefinancial entity. In some embodiments, database(s) 160 recordshistorical loan applications submitted to the financial entity alongwith their corresponding status updates. For example, when therelationship manager at the financial entity or a salesperson (e.g., asales associate) at the dealership works on a corresponding loanapplication recorded in database 160, records stored in database(s) 160can be updated accordingly.

In some embodiments, processor 122 may also specify different categoriesof descriptive attributes of historical loan applications for predictivemodel 126 in order to identify characteristics that are relevant to theoutcomes of the historical loan applications (e.g., funded or notfunded). One of the categories may include, for example, applicant'scredit information, such as a FICO® score, a credit rating, or the like.Other categories may include, for example, deal structure (e.g., loanamount, down payment, annual percentage rate, etc.), line of creditinformation (including, e.g., whether the applicant has a business lineof credit through the financial entity), approval status (including,e.g., whether the loan is approved with any conditions or the like),dealer information (including, e.g., service levels, dealer credit,etc.), and dynamic features that may change over time (including, e.g.,age of the loan application, level of dealer engagement, etc.). It iscontemplated that additional or alternative features that can capturethe dealer relationship, deal structure, and credit information, mayalso be specified without departing from the spirit and scope of thepresent disclosure.

In some embodiments, processor 122 may further specify a category ofdescriptive attribute referred to as “engagement information.”Engagement information for a loan application may indicate, for example,whether or not a relationship manager engaged the dealership (or moregenerally, the loan-arranging entity) to work on the loan application.In some embodiments, the engagement information may be recorded indatabase 160 and processor 122 may retrieve the engagement informationfrom database 160 to form a part of the training data.

In some embodiments, the engagement information for a loan applicationmay further include data regarding whether a relationship managerproactively or reactively engaged the dealership (or more generally, theloan arranging entity) to work on the loan application. Processor 122may, for example, request the relationship managers to indicate whetherthey proactively or reactively engaged the loan arranging entities towork on loan applications by providing corresponding messages via userinterface 144. The relationship managers may provide or update theengagement information via user interface 144, and processor 122 mayrecord the received engagement information in database 160 accordingly.

It is contemplated that knowing whether a relationship managerproactively or reactively engaged a dealership to work on a loanapplication may further improve the accuracy of predictive model 126.Generally, if a dealership contacts a relationship manager to solicitassistance during the underwriting and funding process (in other words,the relationship manager “reactively” engages the dealership to work onthe loan application), the relationship manager may have a greaterchance of winning the bid for the financial entity to fund the loanapplication. On the other hand, more effort may be required of therelationship manager if the relationship manager “proactively” engagesthe dealership to work on the loan application. This is because thedealership may have already started working with another institution tofund the loan, in which case the dealership may only consider therelationship manager's bid if the bid is more attractive compared tothat offered by the other institution. It is also possible that the loanhas already been funded by the other institution, in which caseproactively engaging the dealership to work on the loan application willnot be worthwhile for the relationship manager. Therefore, knowingwhether the relationship manager proactively or reactively engageddealerships to work on loan applications may improve the accuracy ofpredictive model 126. And in some embodiments, processor 122 may buildpredictive model 126 using training data that contains engagementinformation for the historical loan applications.

In some embodiments, processor 122 may organize the training data as aset of “snapshots.” For example, in some embodiments, processor 122 mayorganize the training data as a set of ten snapshots. Each snapshot mayinclude historical loan applications previously pending in a workingqueue of a relationship manager. Furthermore, in some embodiments, eachsnapshot may specify a time window, and only historical loanapplications pending within the time window may be included in thatsnapshot. For example, the time window may be specified as a seven-daywindow. In this manner, the training data may include a snapshotcontaining historical loan applications pending in the working queue ofthe relationship manager within the last seven days. The snapshot mayalso contain information regarding the various categories of descriptiveattributes associated with the historical loan applications. Suchdescriptive attributes may include, e.g., applicant's creditinformation, deal structure, line of credit information, approvalstatus, dealer information, as well as dynamic features, as describedabove.

In some embodiments, processor 122 may create snapshots according to apredetermined interval. For example, in some embodiments, processor 122may create a snapshot every three days (with each snapshot covering aseven-day window) so that there is an overlap of loan applicationsbetween two adjacent snapshots. This overlap may provide continuity thatallows predictive model 126 to analyze loan applications as theyprogress through the approval process, which may in turn help improvethe accuracy of predictive model 126. In some embodiments, processor 122may discard older snapshots and keep only a predetermined number ofsnapshots in the training data. In some embodiment, processor 122 maykeep ten snapshots in the training data. It is to be understood,however, that processor 122 may keep a different number of snapshotswithout departing from the spirit and scope of the present disclosure.

Additionally, in some embodiments, processor 122 may select to includeonly matured loan applications in the training data. Matured loanapplications may include applications that have either been funded bythe relationship manager's financial entity or have expired or beenrejected without being funded by the financial entity. In someembodiments, processor 122 may exclude loan applications that have beencancelled.

In some embodiments, processor 122 may filter the training data to onlyinclude loan applications that are consistent with the relationshipmanager's lines of business. For example, if the relationship manageronly works with certain types of loan applications (e.g., truck loanapplications), processor 122 may select to include only those types ofloan applications in the training data. It is contemplated thatfiltering training data in this manner may help tailor predictive model126 for a particular relationship manager, which may in turn helpimprove the accuracy of predictive model 126 for that particularrelationship manager.

Once processor 122 finishes building predictive model 126, processor 122may utilize predictive model 126 to determine the score of each loanapplication. In some embodiments, the score may be determined based on adifference between the probability of the financial entity funding a newloan application if the financial entity proactively engages the loanarranging entity of the loan application and the probability of thefinancial entity funding the loan application if the financial entityreactively engages the loan arranging entity. The score can thus be usedto indicate whether it is worthwhile for the relationship manager toproactively engage with the loan arranging entity.

Generally, a greater score suggests that proactively engaging thedealership will produce a greater impact on the probability of thefinancial entity funding the loan application. It is to be understoodthat processor 122 may continue to train predictive model 126 on anongoing basis as new records are stored in database 160.

In some embodiments, processor 122 may apply additional mathematicaloperations to calculate the score for each loan application, so that thescore can be further used to indicate a financial value for the loanapplication. For example, in some embodiments, processor 122 maymultiply original scores (e.g., the scores calculated based on theprobabilities of funding new loan applications) by appropriate scalars.In some embodiments, processor 122 may determine the scalars based onthe type of the loan applications. For example, personal loans andcommercial loans may be assigned different scalars by the financialentity. Alternatively, or additionally, loans for different dollaramounts may be assigned different scalars.

In some embodiments, processor 122 may also consider the expectedprofitability of the loans and scale the financial values of the loanapplications accordingly. This “profitability scalar” may be used toincrease the financial value of a loan that would otherwise garner alower financial value or vice versa. For example, in some embodiments,processor 122 may score a highly profitable loan with a higher financialvalue compared to a loan that has a lower profitability, even ifpredictive model 126 suggests that the highly profitable loan is lesslikely to be funded compared to the loan that has the lowerprofitability.

In some embodiments, processor 122 may further adjust the financialvalues of the loan applications based on certain characteristics of theloan applications. For example, if the relationship manager has severalloan applications from the same dealership, processor 122 may recognizethese loan applications as a group and increase the financial values ofthis group of loan applications. For example, processor 122 may increasethe financial values for a group of loan applications because therelationship manager may be able to handle this group of loanapplications more efficiently (e.g., with a single phone call to thedealership rather than multiple phone calls to multiple dealerships). Itis contemplated that the adjustments discussed above are merely examplesand not meant to limit the scope of the present disclosure. Processor122 may increase or decrease the financial values of the loanapplications based on other characteristics of the loan applicationswithout departing from the spirit and scope of the present disclosure.

Processor 122 may further apply time factor adjustments to calculate thescores. For example, in some embodiments, processor 122 may divide thefinancial value calculated for each loan application by an averageworking time required to work on loan applications of the same type. Inthis manner, processor 122 may be able to calculate and finalize thescore that can represent the financial value of the loan application perunit of time. Thus, based on the finalized score, it can be determinedwhich loan applications can benefit the most from the relationshipmanager's proactive engagement per unit of time.

In some embodiments, processor 122 may calculate and report the scorefor each loan application to the relationship manager through userinterface 144. The relationship manager may interact with user interface144 to rank the loan applications based on their corresponding scores indifferent display view. For example, in a default view, the loanapplications may be listed in user interface 144 in a descending orderbased on the scores. On the other hand, in a linked view, two or moreloan applications can be grouped if a grouping condition is satisfiedand listed in user interface 144 together. In this manner, therelationship manager may determine which loan applications can benefitthe most from the relationship manager's proactive engagement based onthe predicted scores.

For further understanding of operations of processor 122 in system 100,reference is made to FIG. 3, which is a flowchart of an exemplary method300 of processing items in the queue, consistent with some embodimentsof the present disclosure. In some embodiments, method 300 may beperformed by system 100 depicted in FIG. 1 or cloud-computing system 200in FIG. 2. For example, processor 122 can be configured to execute a setof instructions stored in memory device(s) 124. When the instructionsare executed, processor 122 can perform operations disclosed in method300. In the following description, reference is made to certaincomponents of FIG. 1 for purposes of illustration. It should beappreciated, however, that other implementations are possible, includingcomponents other than those illustrated in FIG. 1. In some embodiments,method 300 may be performed by a Software as a Service (SaaS) platform,where the processing occurs on a server cloud (e.g., cloud-computingsystem 200 in FIG. 2).

In some embodiments, method 300 includes steps 310, 320, 330, 340, 350,360, 370 and 380. At step 310, processor 122 is configured to analyze aset of training data to build a predictive model (e.g., predictive model126 in FIG. 1) using one or more machine learning algorithms. Asdiscussed above, predictive model 126 may be built by utilizing amachine-learning system (e.g., a gradient boosting machine) using thetraining data including snapshots of historical data obtained atdifferent times. Each snapshot may be created according to rulesdescribed above and include historical loan applications previouslypending in a working queue of a user (e.g., the relationship manager).In some embodiments, applications included in the snapshots have beeneither funded, expired or been rejected.

At step 320, processor 122 is configured to receive items (e.g., newloan applications) in a queue, and information of the items. Informationof the items may include tasks or actions of the items, one or moreparties corresponding to the items, and the like. In some embodiments,the financial entity may receive loan applications from different loanarranging entities (e.g., dealerships). Particularly, in someembodiments, the loan arranging entities may send the loan applicationsto one or more intermediaries, which may then distribute the loanapplications to multiple financial institutions. The financial entitymay record the received information in database(s) 160. Processor 122may then obtain characteristic information of the loan applicationspending in the queue, such as applicant's credit information, dealstructure, dealer information, engagement information, other dynamicfeatures, and the like. In some embodiments, some or all the informationof the loan applications can be accessed or viewed by the responsiblerelationship manager(s) via user interface 144.

At step 330, processor 122 is configured to apply the trained predictivemodel (e.g., predictive model 126 in FIG. 1) to received items in thequeue to determine scores of the items based on respective probabilitiesof an entity (e.g., the financial entity) completing the action or taskfor each item. In some embodiments, processor 122 may apply the trainedpredictive model to prioritize the items in the queue based on thedetermined scores of the items. As discussed above, in some embodiments,the score of each new loan application can be multiplied by a scalarprovided for that loan application, adjusted based on certaincharacteristics (e.g., the expected profitability and the like) of thatloan application, and/or divided by an average working time required forthat loan application. The items can be sorted based on correspondingscores and then stored in an updated queue (e.g., working queue 128 inFIG. 1).

While step 330 may be carried out upon receiving a new application todetermine the score of the new loan application, in some embodiments,step 330 may also be carried out when an existing application receivesupdated information. For example, if one loan application receives aco-signer, the score may be re-evaluated. Likewise, if the dealershipresponds to terms proposed by the relationship manager for one loanapplication, the score may be re-evaluated based on the response. Step330 may also be carried out in other instances to evaluate orre-evaluate loan applications without departing from the spirit andscope of the present disclosure.

In some embodiments, step 330 may be carried out to re-evaluate loanapplications throughout their lifecycle. In some embodiments, the age ofthe loan applications may be a dynamic feature taken into considerationby step 330, which may update the scores calculated for the loanapplications as the loan applications progress through their lifecycle.In some embodiments, users (e.g., relationship managers) may alsoutilize a refresh button provided on user interface 144 to initiate there-evaluation process.

In some embodiments, processor 122 may perform steps 340-360 to presentthe prediction results to one or more users (e.g., relationshipmanagers), in different display views to help the user(s) to prioritizethe actions or tasks. For example, a relationship manager may decidewhether to proactively engage with the corresponding loan arrangingentity for any of the loan applications, having pending actions, in hisor her working queue 128. When the relationship manager interacts withuser interface 144, items in working queue 128 can be presented andlisted in a default view or a linked view to help the relationshipmanager to engage with different loan arranging entities listed inworking queue 128.

Particularly, in some embodiments, at step 340, processor 122 isconfigured to determine whether a linked view is selected by the user.For example, the user can configure and switch between the default viewor the linked view through user interface 144. For ease ofunderstanding, the default view and the linked view will be discussed indetail below in conjunction with FIGS. 4 and 5.

If the linked view is not selected (340—no), at step 350, processor 122is configured to list the items, sorted by the scores, in a firstdisplay view (e.g., the default view) to the user (e.g., a relationshipmanager) through user interface 144.

Reference is made to FIG. 4. FIG. 4 is a schematic diagram illustratingan exemplary user interface 400 in the first display view that may beutilized to present the loan applications to a relationship manager,consistent with some embodiments of the present disclosure. Therelationship manager may utilize interface 400 to negotiate with theloan arranging entities (e.g., dealerships). As shown in FIG. 4, userinterface 400 includes an application list 410, one or more data filters420, a switch 430, types of data categories 440, and a button 450.

For each of the listed loan applications in application list 410, typesof data categories 440 indicate columns for the time information of thelatest update (“DATE”), the dealer's name and relevant information(“DEALER”), the submitter's name and his or her contact information(“SUBMTTER”), the customer's name and his or her reference number(“CUSTOMER”), the application type (“TYPE”), e.g., personal loans orcommercial loans, the application tier (“TIER”), which may be determinedbased on the dollar amounts of the loans and the types of the loans, thecounteroffer information (“CALLBACK”), the hyperlink directed tonegotiation messages (“MESSAGE”), and whether the application is lockedor archived (“STATE”). It is noted that while information such as theapplication tier may affect the score of the application in someembodiments, the application tier itself does not indicate the actualscore determined by processor 122.

The listed loan applications, which are ranked and listed in applicationlist 410 in a descending order according to the scores, may remainpending in the queue until they are funded, expired, rejected,cancelled, or otherwise acted upon by either the relationship manager orthe dealership. The relationship manager may provide a response back tothe dealership with proposed loan terms for one of the listed loanapplications in application list 410 and wait for further communicationsfrom the dealership.

As shown in FIG. 4, processor 122 is configured to display respectiveloan arranging entities and submitters of the loan applications in userinterface 400 in the default view. In some embodiments, the user may,for example, apply one or more data filters 420 made available throughuser interface 400. For example, the user may apply one or more datafilters 420 to request display of only loan applications sent from acertain dealerships or intermediaries. In some embodiments, the user mayhave the option to turn off the ranking using switch 430 provided onuser interface 400. The user may then choose to sort the loanapplications using various types of data categories 440, such as dateinformation, dealer information, submitter information, customerinformation, or tiers of the loans applications without departing fromthe spirit and scope of the present disclosure.

The user may send a command through user interface 400 to switch backand forth between the default view and the linked view. For example, theuser may provide such command by clicking a button 450 provided on userinterface 400. In various embodiments, other interactive means,including selecting lists, checkboxes, etc., can also be provided onuser interface 400 to achieve the switching between different displayviews. In response to the command from the user through user interface400, processor 122 is configured to switch user interface 400 from thedefault view to the linked view.

Referring to FIG. 3 again, if the linked view is selected (340—yes),then at step 360, processor 122 is configured to identify apredetermined number of items having the highest scores. For example, insome embodiments, processor 122 is configured to identify a first itemhaving the highest score, and a second item having the next highestscore. It is appreciated that the number of items to be identified maybe different in various embodiments. For example, in some embodiments,processor 122 is further configured to identify a third item having athird highest score, and so on.

At step 370, processor 122 is configured to group other ones of theitems that satisfy a grouping condition based on characteristicinformation of the items, with each of the identified items having highscores. For example, processor 122 can group with each of the first andsecond items, one or more corresponding items that satisfy the groupingcondition.

In embodiments where the first and second items are first and secondloan applications pending in the queue, the grouping condition may bethe submitter information or the loan arranging entity information. Inone embodiment, processor 122 can identify loan arranging entities ofthe loan applications. If one of the loan applications corresponds tothe same loan arranging entity as that of the first loan application orthe second loan application, processor 122 is configured to group itwith the first loan application or the second loan applications. Inanother embodiment, processor 122 can identify submitters of the loanapplications. If one of the loan applications corresponds to the samesubmitter as that of the first loan application or the second loanapplication, processor 122 is configured to group it with the first loanapplication or the second loan applications.

At step 380, processor 122 is configured to list the groups of itemsincluding the first and second items in the second display view (e.g.,the linked view) to the user (e.g., the relationship manager) throughuser interface 144. The group including the first item, hereafterreferred to a first group, and the group including the second item,hereafter referred to a second group, can be sorted based on the scoresof the first item and the second item. For example, as the score of thefirst item is greater than the score of the second item, items in thefirst group can be listed at the top of the queue in user interface 144,and items in the second group can be listed below the first group inuser interface 144.

In some embodiments, when listed in user interface 144, the items in thesame group can also be sorted and presented based on their scores. Forexample, processor 122 can be configured to list multiple items in thesame group in a descending order according to the scores of the items,so the item having the highest score in the group is listed as the topitem in the group, and so on.

As discussed above, in some embodiments, processor 122 may identifythree or more items having high scores. If a third item having a thirdhighest score is identified, at step 370, processor 122 is furtherconfigured to group the items that satisfy the grouping condition basedon the characteristic information with the third item. At step 380,processor 122 is further configured to list the group of the itemsincluding the third item in the second display view below the secondgroup, to the user through user interface 144.

Reference is made to FIG. 5. FIG. 5 is a schematic diagram illustratingan exemplary user interface 500 in the second display view that may beutilized to present the loan applications to a relationship manager,consistent with the disclosed embodiments. Similar to user interface 400in FIG. 4, as shown in FIG. 5, processor 122 is configured to displayrespective loan arranging entities and submitters of the loanapplications in user interface 500 in the linked view. User interface500 also provides an application list 510, one or more data filters 520,a switch 530, data categories 540 and button 550 for switching betweendifferent views. In FIG. 5, after pressed by the user, button 550 ishighlighted, indicating that user interface 500 presents information inthe linked view.

As shown in FIG. 5, two loan applications having the same “Dealer ABC”are grouped and displayed together with a linking chain mark showingthat the two loan applications are “linked.” Thus, when the relationshipmanager reaches out to the “Dealer ABC” to discuss the loan applicationhaving the highest score, the relationship manager can also discussanother loan application with the same dealer.

Similarly, three loan applications having the same “Dealer DEF” aregrouped and displayed together with linking chain marks showing that thethree loan applications are “linked.” When the relationship managerreaches out to the “Dealer DEF” to discuss the loan application havingthe next highest score, the relationship manager can also discuss otherloan applications with the same dealer. Thus, the linked view providedin user interface 500 can facilitate the engagement process with loanarranging entities and avoid repeated calls to the same dealer.

As shown in FIG. 5, in some embodiments, processor 122 may list itemssorted by the scores in each of the groups in the second display view.For example, in the three loan applications having the same “DealerDEF,” the loan application for “CUSTOMER 2” has the highest score, theloan application for “CUSTOMER 8” has the second highest score, and theloan application for “CUSTOMER 9” has the third highest score.Accordingly, the three loan applications in the same group are rankedand sorted by the scores in the second display view. As noted above, thescore itself is not included in the list.

In addition, in some embodiments, processor 122 may limit the number ofthe listed items in each of the groups to be within a threshold value.For example, while there may be more than three loan applications havingthe same “Dealer DEF,” processor 122 may only present the top three loanapplications in the group in the linked view. It is appreciated that thethreshold value can be customized and adjusted in different embodimentsbased on need, and the embodiments discussed herein are merely examplesand not meant to limit the scope of the present disclosure. In differentembodiments, processor 122 may present 2, 3, 4, 5, or any possiblenumber of listed items in each group. In some embodiments, differentgroups may also apply different threshold values.

It is also appreciated that different grouping conditions can be set invarious embodiments. For example, in some embodiments, loan applicationshaving the same submitter are grouped and displayed together with alinking chain mark showing that the loan applications are “linked.”Grouping conditions based on different types of characteristic of theitems may be configured by the user through user interface 400 or 500.The grouping conditions discussed above and illustrated in FIG. 5 aremerely examples and not meant to limit the scope of the presentdisclosure.

Similar to the operations discussed above, the user may provide acommand by clicking button 550 provided on user interface 500 to achievethe switching between different display views. In response to thecommand from the user through user interface 500, processor 122 isconfigured to switch user interface 500 from the linked view back to thedefault view.

It is noted that while the calculated scores are only applied in thebackground for ranking and not shown in user interfaces 400 and 500 inFIG. 4 or FIG. 5, the embodiments discussed above are merely examplesand not meant to limit the scope of the present disclosure. In someembodiments, the calculated score for each application may be displayedin user interfaces 400 and 500 to provide more information for assistingthe relationship manager.

Accordingly, by performing method 300 described above, system 100 orcloud-computing system 200 can process multiple items in the queue torank items, and group items to be presented to the user in response tocommands from the user. In view of the above, systems and methods forprocessing items in a queue disclosed in various embodiments of thepresent disclosure provide multiple display views to list items indifferent ways to satisfy the needs in different scenarios. By switchingthe display views, the disclosed systems and methods can provide a moreflexible user interface to help the user prioritize and complete tasksor actions of the items and improve the efficiency of managing itemsassociated with different external parties in the working queue.

The various example embodiments herein are described in the generalcontext of systems, method steps, or processes, which may be implementedin one aspect by a computer program, an application, a plug-in module orsubcomponent of another application, or a computer program product whichis embodied in a transitory or a non-transitory computer-readablemedium, including computer-executable instructions, such as programcode, executed by computers in networked environments. Acomputer-readable medium may include removable and nonremovable storagedevice(s) including, but not limited to, Read Only Memory (ROM), RandomAccess Memory (RAM), compact discs (CDs), digital versatile discs (DVD),etc. Generally, program modules may include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of program code for executing steps of the methods disclosedherein. The particular sequences of such executable instructions orassociated data structures represent examples of corresponding acts forimplementing the functions described in such steps or processes.

As used herein, unless specifically stated otherwise, the term “or”encompasses all possible combinations, except where infeasible. Forexample, if it is stated that a database may include A or B, then,unless specifically stated otherwise or infeasible, the database mayinclude A, or B, or A and B. As a second example, if it is stated that adatabase may include A, B, or C, then, unless specifically statedotherwise or infeasible, the database may include A, or B, or C, or Aand B, or A and C, or B and C, or A and B and C.

In the foregoing drawings and specification, embodiments have beendescribed with reference to numerous specific details that can vary fromimplementation to implementation. It will be apparent to those skilledin the art that various adaptations and modifications of the describedembodiments can be made. Other embodiments will be apparent to thoseskilled in the art from consideration of the specification and practiceof systems and related methods disclosed herein. It is intended that thesequence of steps shown in figures are only for illustrative purposesand are not intended to be limited to any particular sequence of steps.As such, those skilled in the art can appreciate that these steps can beperformed in a different order while implementing the same or similarmethods. It is intended that the specification and examples beconsidered as exemplary only, with a true scope being indicated by thefollowing claims and their equivalents.

1-20. (canceled)
 21. A system for processing items in a queue,comprising: one or more databases including training data storedtherein; one or more memory devices storing instructions; one or moreprocessors configured to execute the instructions; one or moreinterfaces that manage interactions between the one or more processorsand the one or more databases; and the one or more processors configuredto execute the instructions to perform operations comprising: analyzingthe training data stored in the one or more databases and building apredictive model based on the analyzing of the training data, whereinthe analyzing of the training data includes building the predictivemodel utilizing a machine-learning system trained using the trainingdata; applying the predictive model to a plurality of items in a queueof items and determining scores of the plurality of items based onrespective probabilities of an entity completing an action for each ofthe plurality of items; generating a first display view to a user on auser interface of the one or more interfaces, the first display viewlisting the plurality of items that are sorted by the scores;identifying a first and a second item, of the plurality of items,respectively having highest and next highest scores; and grouping, witheach of the first and second items, other ones of the plurality of itemsthat satisfy a grouping condition based on characteristic information ofthe plurality of items, wherein the machine-learning system comprises agradient boosting machine that uses a machine-learning technique forregression and data classification.
 22. The system of claim 21, theoperations further comprising switching between the first display viewand a second display view on the user interface of the one or moreinterfaces in response to a command from the user through the userinterface.
 23. The system of claim 21, wherein the operations furthercomprises listing the plurality of items that are sorted by the scoresin a descending order of the scores in the first display view.
 24. Thesystem of claim 21, the operations further comprising displaying thescores of the plurality of items in the user interface in the firstdisplay view or in a second display view on the user interface of theone or more interfaces.
 25. The system of claim 21, the operationsfurther comprising: identifying a third item, of the plurality of items,having a third highest score; grouping, with the third item, other onesof the plurality of items that satisfy the grouping condition based onthe characteristic information; and listing the group of the pluralityof items including the third item in a second display view on the userinterface of the one or more interfaces.
 26. The system of claim 21,wherein the operations further comprising listing, in each of thegroups, the ones of the items sorted by the scores in a descending orderof the scores, in a second display view on the user interface of the oneor more interfaces.
 27. The system of claim 21, wherein the gradientboosting machine is configured to produce the predictive model as anensemble of predictive models which include decision trees forpredicting the probabilities of the loan arranging entity funding theplurality of loan applications without proactive engagement of a userworking for the loan arranging entity completing the action.
 28. Thesystem of claim 21, wherein the machine-learning system is furtherconfigured to produce the predictive model using at least one oflogistic regression, naive Bayes, neural networks, support vectormachines, or random forests.
 29. The system of claim 21, wherein theoperations further comprising: displaying, in each of the groups, theones of the items sorted by the scores in a second display view on theuser interface of the one or more interfaces with a linking chain markshowing that the ones of the items are linked.
 30. The system of claim21, wherein the operations further comprising: generating a seconddisplay view to the user on the user interface of the one or moreinterfaces, the second display view listing the groups of itemsincluding the first and second items that are sorted based on the scoresof the first and second items; listing, in each of the groups, ones ofthe items sorted by the scores, in the second display view; and limitingthe number of the listed items in each of the groups to be within athreshold value.
 31. A method for processing items in a queue,comprising: receiving characteristic information of a plurality ofitems; building a predictive model utilizing a machine-learning system;applying the predictive model to the plurality of items and determiningscores of the plurality of items based on respective probabilities of anentity completing an action for each of the plurality of items;generating a first display view to a user on a user interface of one ormore interfaces, the first display view listing the plurality of itemsthat are sorted by the scores; identifying a first and a second item, ofthe plurality of items, respectively having highest and next highestscores; and grouping, with each of the first and second items, otherones of the plurality of items that satisfy a grouping condition basedon characteristic information of the plurality of items, wherein themachine-learning system comprises a gradient boosting machine that usesa machine-learning technique for regression and data classification. 32.The method of claim 31, further comprising switching between the firstdisplay view and a second display view on the user interface of the oneor more interfaces in response to a command from the user through theuser interface.
 33. The method of claim 31, further comprising listingthe plurality of items that are sorted by the scores in a descendingorder of the scores in the first display view.
 34. The method of claim31, further comprising training the machine-learning system using thecharacteristic data.
 35. The method of claim 31, wherein the listing thegroups further comprises: identifying a third item, of the plurality ofitems, having a third highest score; grouping, with the third item,other ones of the plurality of items that satisfy the grouping conditionbased on the characteristic information; and listing the group of theplurality of items including the third item a second display view on theuser interface of the one or more interfaces.
 36. The method of claim31, further comprising listing, in each of the groups, the ones of theitems sorted by the scores in a descending order of the scores, in asecond display view on the user interface of the one or more interfaces.37. The method of claim 31, further comprising listing the plurality ofitems that are sorted by the scores in a descending order of the scoresin the first display view.
 38. The method of claim 31, wherein thegradient boosting machine is configured to produce the predictive modelas an ensemble of predictive models which include decision trees forpredicting the probabilities of the loan arranging entity funding theplurality of loan applications without proactive engagement of a userworking for the loan arranging entity completing the action.
 39. Themethod of claim 31, further comprising displaying, in each of thegroups, the ones of the items sorted by the scores in a second displayview on the user interface of the one or more interfaces with a linkingchain mark showing that the ones of the items are linked.
 40. Anon-transitory computer-readable medium storing instructions that areexecutable by one or more processors to perform operations comprising:building a predictive model utilizing a machine-learning system trainedusing the respective probabilities of an entity completing an action foreach of a plurality of items; determining scores of the plurality ofitems based on the respective probabilities of the entity completing theaction for each of the plurality of items; generating a first displayview to a user on a user interface of one or more interfaces, the firstdisplay view listing the plurality of items that are sorted by thescores; identifying a first and a second item of the plurality of itemsrespectively having highest and next highest scores; and grouping, witheach of the first and second items, other ones of the plurality of itemsthat satisfy a grouping condition based on characteristic information ofthe plurality of items, wherein the machine-learning system comprises agradient boosting machine that uses a machine-learning technique forregression and data classification.